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Pore detection in 3‐D CT soil samples through an improved sub‐segmentation method
Author(s) -
OjedaMagaña B.,
Quintanilla Domínguez J.,
Ruelas R.,
MartínSotoca J. J.,
Tarquis A. M.
Publication year - 2019
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/ejss.12728
Subject(s) - segmentation , artificial intelligence , cluster analysis , computer science , sample (material) , scanner , pattern recognition (psychology) , robustness (evolution) , image segmentation , computer vision , soil test , geology , soil science , soil water , physics , chemistry , biochemistry , gene , thermodynamics
Summary X‐ray computer tomography (CT) is a non‐invasive technique for image acquisition. Recent technological advances have enabled reliable and high‐resolution images to be obtained. In soil samples, for example, this subserves the identification of pores and their structure and the analysis of their geometric characteristics. However, the lack of contrast between pores and solids in soil samples makes it difficult to identify the pores, and it poses problems for their connectivity when a three‐dimensional (3‐D) reconstruction is made from a group of consecutive 2‐D images obtained with a scanner. To solve this problem, an improved sub‐segmentation method, which had been developed and tested previously, was applied in this research to achieve a better identification of the pore space and consequently the solid space in the 2‐D slices of the image, followed by a 3‐D reconstruction of the soil sample. In this study, two soil samples were used, one real soil sample with 255 2‐D CT consecutive images and a synthetic image with 215 2‐D images. The latter sample was used only to evaluate the robustness of the improved sub‐segmentation method and the results from analysis of the pore connectivity in a known structure. The results obtained with the improved sub‐segmentation were compared with those of traditional clustering algorithms for image segmentation by k ‐means, fuzzy c ‐means and Otsu's methods. The results were promising, and the 3‐D reconstruction presents a realistic structure for the continuity and coincidence of the shapes of the pores in the consecutive images. In addition, the pore regions detected have a small non‐uniformity (NU) value, which indicates both good pore detection and homogeneity, which facilitates pore connectivity between the different 2‐D images. Highlights Pore structure is not disturbed by the acquisition of images with computer tomography. Pore spaces were identified properly with the improved sub‐segmentation. Pore continuity by position, size and shape was observed through the 2‐D consecutive images. Realistic 3‐D reconstruction was feasible for the pore spaces in soil samples.